During the performance of examinations using medical devices, e.g. CT systems, large quantities of data are generated in a very short time with the rotating detector, since the detector has a plurality of channels, which are read out at short sampling intervals during an examination. This data must be transmitted from the rotating part, the gantry of the CT system, or from the detector, by way of a transmission link to the non-rotating part of the CT system, i.e. to the image reconstruction facility, which creates the images to be reconstructed from this data. This transmission should proceed as quickly as possible, but at the same time there should be the smallest possible loss of signal quality for the data, e.g. any increase in noise due to the data transmission process. To design the transmission link, which has slip rings, between the rotating part and the non-rotating part as economically as possible in respect of bandwidth and capacity, it is necessary to reduce the quantity of data, for example by way of compression algorithms.
Currently the data for transmission from the gantry to the data processor is preprocessed so that an offset value is subtracted from the original 20-bit data of the analog/digital converter. The result is logarithmized by a transformation and thus scaled into a 16-bit data space. This process reduces the quantity of data significantly but there is what is known as a clipping of negative values. At high data speeds, e.g. with short sampling times or full detector utilization, an additional compression algorithm is used to compress the data further, so that the capacity of the available transmission link, i.e. the slip rings, is sufficient for the data transmission to keep pace during the examination. On the receiver side this additional compression is first reversed in what is known as a receiver, which is connected upstream of the data processor. The data is then forwarded to the data processor in a logarithmized data format. There, the data is then either further processed in logarithmic form or back transformed to the original linear scale, i.e. delogarithmized (exponentialized).
Although the provision of logarithmic data is highly suitable for further processing, i.e. image reconstruction, this procedure has the following disadvantages:
1. Noise can cause negative measurement values to occur with a low input signal. Since no real logarithm is defined for numbers less than or equal to zero, such values are set to a fixed value>zero (clipping). This means that during subsequent fusing (=combining of a number of adjacent pixels as part of image reconstruction) it is not possible to achieve satisfactory noise reduction.2. There are specific correction algorithms, such as scatter beam correction, EFS filters, etc., in which, in addition to the logarithmic data, it is also necessary to represent the data on a linear intensity scale, i.e. provide linear data. The additional transformation of the logarithmized to linear data necessitates an unwanted additional time outlay.